Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography.

ABSTRACT: Purpose: This study aimed to explore the ability of texture parameters combining with machine learning methods in distinguishing intrahepatic cholangiocarcinoma (ICCA) and hepatic lymphoma (HL). Method: A total of 28 patients with HL and 101 patients with ICCA were included. A total of 45 texture features were extracted by the software LifeX from contrast-enhanced computer tomography (CECT) images and 38 of them were eligible. A total of 5 feature selection methods and 9 feature classification methods were used to build the best diagnostic models, combining with the 10-fold cross-validation to assess the accuracy of these models. The discriminative ability of each model was evaluated by receiver operating characteristic analysis. Result: A total of 45 predictive models

SUBMITTER: Xu H 

PROVIDER: S-EPMC8435928 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature